EFFECT OF STAIRCASE ON RC FRAME STRUCTURES UNDER SEISMIC LOAD

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Proceedings-International Conference on Trends and challenges in Concrete Structures Ghaziabad, UP, India December 19-21, 2013 organised by ICI

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EFFECT OF STAIRCASE ON RC FRAME STRUCTURES UNDER SEISMIC LOAD

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Flood frequency and its magnitude are essential for the proper design of hydraulics structures such as bridges, spillways, culverts, waterways, roads, railways, flood control structures and urban drainage systems. Since, flood is a very complex natural event depending upon characteristics of catchment, rainfall conditions and various other factors, thus its analytical modelling is very difficult to pursue. Recently, artificial intelligence techniques such as gene expression programming (GEP), artificial neural network (ANN) etc. have been found to be efficient in modelling complex problems in hydraulic engineering. The performance of GEP model has been reported to be better than that of the ANN. Moreover, GEP provides mathematical equation which makes it more superior over other soft computing techniques that do not give any analytical mathematical equation. Therefore, in present study, GEP is implemented in flood frequency analysis for typical Indian river gauging station. The results obtained in the present study are highly promising and suggest that GEP modelling is a versatile technique and represents an improved alternative to the more conventional approach for the flood frequency analysis.

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The scour modelling in cohesive beds is relatively more complex than that in sandy beds and thus there is limited number of studies available on local scour at bridge abutments on cohesive sediment. Recently, a good progress has been made in the development of data-driven techniques based on artificial intelligence (AI). It has been reported that AI-based inductive modelling techniques are frequently used to model complex process due to their powerful and non-linear model structures and their increased capabilities to capture the cause and effect relationship of such complex processes. Gene Expression Programming (GEP) is one of the AI techniques that have emerged as a powerful tool in modelling complex phenomenon into simpler chromosomal architecture. This technique has been proved to be more accurate and much simpler than other AI tools. In the present study, an attempt has been made to implement GEP for the development of scour depth prediction model at bridge abutments in cohesive sediments using laboratory data available in literature. The present study reveals that the performance of GEP is better than nonlinear regression model for the prediction of scour depth at abutments in cohesive beds.

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